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MLOps: From Models to Production

Acquire the skills to build effective real-world ML systems (bootstrapping datasets, improving label quality, experimentation, model evaluation, deployment and observability) with hands-on projects. This course will help you bridge the gap between state-of-the-art ML modeling, and building real-world ML systems.

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Nihit Desai
CTO of Refuel.AI (ex-Facebook, Stanford)
Price
US$ 400
or included with membership
Duration
Coming soon
Sold out, but you can still join the waitlist!

Course taught by expert instructors

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Affiliation logo

Nihit Desai

CTO of Refuel.AI (ex-Facebook, Stanford)

Nihit Desai is the CTO and co-founder of Refuel.AI, an early stage ML infrastructure startup. Prior to this, he was a Staff Engineer at Facebook where he led ML efforts for content moderation. In prior roles, he has worked on large scale recommender systems at Instagram, and on search quality at LinkedIn. He holds a Masters degree in Computer Science from Stanford, specializing in Artificial Intelligence.

The course

Learn and apply skills with real-world projects.

Who is it for?
  • Software engineers who want to build production systems that integrate ML

  • Data scientists who want to get hands on experience with the production ML lifecycle

  • Students/recent college grads who want to learn about building and shipping ML applications

Prerequisites
  • Knowledge of basic machine learning concepts.

  • Familiarity with software development in Python.

  • Recommended: Familiarity with Docker, cloud ecosystems such as AWS.

Not ready?

Try these prep courses first

Learn
  • Archetypes of real-world ML applications
  • The production ML lifecycle
  • Why data quality and quantity are critical for real-world ML success
A machine learning model to predict news categories from news article text.
  • Exploratory data analysis
  • Model training & hyperparameter optimization
  • Fine-tuning state-of-the-art pretrained transformer models for NLP tasks
Learn
  • Designing good model evaluation metrics
  • Model underfitting and overfitting: what are they, and how to address them
  • Behavioral testing for ML models
Test and evaluate the news classification from Week 1, and conduct error analysis.
  • Establish bounds on model performance with human annotation baseline
  • Behavioral testing for ML models
  • Testing for statistical properties of datasets
Learn
  • Options for deploying models online: common scenarios & tradeoffs
  • Feature Stores
  • Good practices to ensure production stability: gated rollouts, shadow mode deployment, online experimentation, and easy rollbacks
Wrap the trained and tested model from week 2 in a lightweight web service. Deploy the service and test it online.
  • Wrap the model and data pipeline in a python FastAPI web service
  • Containerize the service using Docker
  • Basic integration testing for containerized service
  • Deploy the service and test it online
Learn
  • How MLOps practices evolve as a function of team and company maturity
  • Logging and monitoring infrastructure for ML applications
  • Data and concept drift in Machine Learning
  • CI/CD for ML models
Monitoring and online performance tracking in ML systems.
  • Statistical data and concept drift measures
  • Model performance measurement
  • Outlier detection

A course you'll actually complete. AI-powered learning that drives results.

AI-powered learning

Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.

Live courses by leading experts

Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.

Practice on the cutting edge

Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.

Flexible schedule for busy professionals

We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.

Timeline

Completion certificates

Each course comes with a certificate for learners to add to their resume.

Best-in-class outcomes

15-20x engagement compared to async courses

Support & accountability

You are never alone, we provide support throughout the course.

Get reimbursed by your company

More than half of learners get their Courses and Memberships reimbursed by their company.

Hundreds of companies have dedicated L&D and education budgets that have covered the costs.

Reimbursement

Course success stories

Learn together and share experiences with other industry professionals

Nihit has a rare set of skills and experiences - building large-scale ML production systems at top companies, along with a solid and rigorous research background. Along with that, he is great at distilling and passing on his hard-won insights and knowledge. I've learned a lot from his newsletter and the talks he's given to large audiences at Upstart - so I know first-hand how valuable and practical this class will be, and can't think of a better instructor!

Poorna KumarSenior Manager, Machine Learning @ Upstart; prev: ML, Statistics @ Stanford

Nihit has extensive experience building ML systems for recommendations, ranking and integrity problems at Facebook and LinkedIn. His expertise lies not only in developing and improving deep learning techniques but also in working with large scale systems that scale to billions of users. It’s a combination of both these skill sets that makes him a great fit to teach an MLOps course that requires an in-depth understanding of ML fundamentals and the ability to build out scalable systems that deal with constantly growing and ever-changing datasets in the real-world.

Neil DhruvaMachine Learning Engineer @ Glean; ex-Facebook

Nihit combines a deep theoretical understanding of ML with hands-on practical knowledge from having built large-scale search, recommender, and decisioning ML systems at the most impactful Internet companies. If I had to learn how to go from an idea to a working, scalable ML system, there would be no better instructor than Nihit!

Rishabh BhargavaCo-Founder and CEO @ ML infra startup; co-editor of MLOpsRoundup

Everything about this course is awesome and exactly what I was looking for. I love how they put into consideration the different levels of experience of participants and set up helpful coding parties. The lectures & content are very detailed. I also learned a lot while trying out the projects, and the community is simply the best. Big thanks to the course manager for checking in and boosting my morale, the TA for leading the weekly coding parties (those were super helpful) and of course, Nihit. Thank you Uplimit!

Gigi KennethMachine Learning Engineer

Amazing course! Addresses well the challenges that exist in the development of a real machine learning pipeline and demonstrates techniques and tools on how to solve it. The community is really helpful!

Patrick SouzaInnovation Engineer @ Bosch

Frequently Asked Questions

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